Comparison Between ARIMA And Fourier ARIMA Model To Forecast The Demand Of Electricity In Sulaimani Governorate

توێژەران

  • Botan Karim Ahmed Department of Information technology, College of Commerce, University of Sulaimani, Sulaimani, Iraq
  • Sham Azad Rahim Department of Statistics and Computer, College of Commerce, University of Sulaimani, Sulaimani, Iraq
  • Bestan Bahaalddin Maaroof Department of Information technology, College of commerce, University of Sulaimani, Sulaimani, Iraq
  • Hindreen Abdullah Taher Department of Statistics and Computer, College of Commerce, University of Sulaimani, Sulaimani, Iraq

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https://doi.org/10.25212/lfu.qzj.5.3.36

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Trend, ARIMA, Stationary, Box-Jenkins Models.

پوختە

Electric energy is accounted as one of the major goods in human life, and also have a great role in progressing and developing the several sectors as economics, manufactures and any other sector related to daily use. In this study the monthly demand of electricity in Sulaimani governorate have been used, the main goal of the study is to choose appropriate model to forecast the monthly demand of electric in Sulaimani governorate for 12 months in 2020, the analyzing,results and comparison shows that FSARIMA(0,0,0)x(2,1,0)4 is appropriate model for this mission of forecasting which has minimum AIC among the other candidate models that equal to 0.28

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سەرچاوەکان

A.D, j., d. F., & tucker, m. (2007). new ARIMA models for seasonal time series and their application to second adjustment and forecasting.

agyemang, b. (2012). autoregrissive moving average (ARIMA) intervention analysis model for the major crimes in chana. math department kwame nkrumah university.

akinmutmi, c. (1996-2011). a sesonal arima modelling of mathematics.

alptekin, k. m. (2007). building cost index forecasting with time series analysis. turkey.

chatfield, C. (1996). the analysis of time series.

cochrane, J. (2005). time serias for macroeconomics and finance. chicago: university of chicago.

cryer, J. D. (2008). time series analysis (2 ed.). new york.

dtreg. (2015, 3). time series analysis.

eni, d., & adeyeye, f. J. (2015). seasonal ARIMA modeling and forecasting of rainfall in warri town nigeria. geoscience and enviroment protection .

etuk, e. H. (2014, 4 18). an additive seasonal box-jenks model for nigerian monthly savings deposit rates.

Reinsel, George. C. (1994). time series analysis forecasting and control. (3, Ed.)

gyasi-agyei, k. (2012). analysis and modeling of prevalence of measles in the ashnti region of chana. west africa : university of science and technology .

hamilton, J. (1994). time serias analysis . amirica united state : princeton university.

hudu, m. (2009). interrupted time series analysis of the rate of inflation in chana. west africa.

hurvich, C., & C.L, T. (1989). regression and time serias model selection in small sample .

mynsbrugge, j. V. (2010). bidding stratrgies using orice based unit commitment in a deregulated power market .

permanasari, A. e., hidayat, i., & alfi bustoni, i. (2013). SARIMA seasonal ARIMA implementation on time series to forcast the number of malaria indidence. information technology and electrical engineering.

petrevska, b., & delcev, g. (2016). predicting tourism demand by A.R.I.M.A models.

sariaslan, n. (2010). the effect of temporal aggregation on univariate time series analysis . turkey.

shumway, R. S. (2006). time series analysis and its applecation. (2, Ed.) new york.

singh, e. h. (2013). fourcasting tourist inflow in bhutan using seasonal ARIMA. international journal of science and resarch (IJSR), 2 (9).

spyros, M. w., & R.J, h. (1998). forecasting model and application. (3, Ed.)

Wei. William. (1989). time series analysis univariate and multivariate method . university of tempie.

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2020-09-30

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